Pinpointing subcellular protein localizations from microscopy images is easy to the trained eye, but challenging to automate. Based on the Human Protein Atlas image collection, we held a competition to identify deep learning solutions to solve this task. Challenges included training on highly imbalanced classes and predicting multiple labels per image. Over 3 months, 2,172 teams participated. Despite convergence on popular networks and training techniques, there was considerable variety among the solutions. Participants applied strategies for modifying neural networks and loss functions, augmenting data and using pretrained networks. The winning models far outperformed our previous effort at multi-label classification of protein localization patterns by ~20%. These models can be used as classifiers to annotate new images, feature extractors to measure pattern similarity or pretrained networks for a wide range of biological applications.
Background
Natural orifice specimen extraction surgery (NOSES) has been successfully applied to the treatment of gastric, colorectal cancer (CRC). However, the development of NOSES is still in the exploratory stage, and there is still no strong evidence-based medical evidence.
Patients and Methods
From January 2013 to June 2017, consecutive patients with colorectal cancer who underwent transluminal resection, anastomosis, and specimen extraction and those who underwent conventional laparoscopic resection were enrolled. Propensity score matching was used to align clinicopathological features between the two groups.
Results
A total of 372 patients were eventually included in this study, 186 in each group. According to perioperative information and postoperative follow-up in both groups, the NOSES group had less blood loss (P = 0.011), shorter time to recovery of gastrointestinal function (P < 0.001), shorter postoperative hospital stay (P = 0.037). The NOSES group had fewer postoperative analgesics (P < 0.001), lower postoperative pain scores (P < 0.001), and lower incidence of postoperative complications (P = 0.017). Compared with the LA (laparoscopic) group, the NOSES group had better physical function (P<0.05), role function (P<0.001), emotional function (P<0.001) and global health status than LA group, while symptoms such as pain (P<0.001), insomnia (P<0.001), constipation (P<0.001) and diarrhea (P<0.05) were less severe in the NOSES group. In addition, the NOSES group had higher body image scores. Overall survival (OS) and disease-free survival (DFS) were not significantly different between the two groups.
Conclusion
For surgical treatment of colorectal cancer, NOSES has advantages in reducing postoperative pain, recovery of gastrointestinal function, postoperative quality of life, and improving patients’ satisfaction with abdominal wall aesthetics. There was no difference in long-term survival between NOSES and conventional laparoscopic surgery.
Background: Lymph node examination is a prognostic indicator for colon cancer (CC) patients. The aim of this study was to develop and validate a preoperative risk prediction model for inadequate lymph node examination. Methods: 24284 patients diagnosed as stage I-III CC between 2010-2014 were extracted from SEER database and randomly divided into development cohort (N=12142) and internal validation cohort (N=12142). 680 patients diagnosed as stage I-III CC between 2012-2014 were extracted from our hospital as external validation cohort. Logistic regression analysis was performed and risk score of each factor was calculated according to model formula. Model discrimination was assessed using C-statistics. Results: Preoperative risk factors were identified as gender, age, tumor site and tumor size. Patients with total risk score of 0-6 were considered as low risk group while patients scored ≥13 were considered as high risk group. The model had good discrimination and calibration in all cohorts and could apply to patients in the SEER database (American population) and patients in our hospital (Chinese population). Conclusions: The model could accurately predict the risk of inadequate lymph node examination before surgery and might provide useful reference for surgeons and pathologists.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.